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Drought monitoring and early detection have improved greatly in recent decades through the development and refinement of numerous indices and indicators. However, a lack of guidance, based on user experience, exists as to which drought-monitoring tools are most appropriate in a given location. This review paper summarizes the results of targeted user engagement and the published literature to improve the understanding of drought across North America and to enhance the utility of drought-monitoring tools. Workshops and surveys were used to assess and make general conclusions about the perceived performance of drought indicators, indices and impact information used for monitoring drought in the five main Köppen climate types (Tropical, Temperate, Continental, Polar Tundra, Dry) found across Canada, Mexico, and the United States. In Tropical, humid Temperate, and southerly Continental climates, droughts are perceived to be more short-term (less than 6 months) in duration rather than long-term (more than 6 months). In Polar Tundra climates, Dry climates, Temperate climates with dry warm seasons, and northerly Continental climates, droughts are perceived to be more long-term than short-term. In general, agricultural and hydrological droughts were considered to be the most important drought types. Drought impacts related to agriculture, water supply, ecosystem, and human health were rated to be of greatest importance. Users identified the most effective indices and indicators for monitoring drought across North America to be the U.S. Drought Monitor (USDM) and Standardized Precipitation Index (SPI) (or another measure of precipitation anomaly), followed by the Normalized Difference Vegetation Index (NDVI) (or another satellite-observed vegetation index), temperature anomalies, crop status, soil moisture, streamflow, reservoir storage, water use (demand), and reported drought impacts. Users also noted the importance of indices that measure evapotranspiration, evaporative demand, and snow water content. Drought indices and indicators were generally thought to perform equally well across seasons in Tropical and colder Continental climates, but their performance was perceived to vary seasonally in Dry, Temperate, Polar Tundra, and warmer Continental climates, with improved performance during warm and wet times of the year. The drought indices and indicators, in general, were not perceived to perform equally well across geographies. This review paper provides guidance on when (time of year) and where (climate zone) the more popular drought indices and indicators should be used. The paper concludes by noting the importance of understanding how drought, its impacts, and its indicators are changing over time as the climate warms and by recommending ways to strengthen the use of indices and indicators in drought decision making. [ABSTRACT FROM AUTHOR]

The frequency of El Niño occurrences in southern Africa surpasses the norm, resulting in erratic weather patterns that significantly impact food security, particularly in Zimbabwe. The effects of these weather patterns posit that El Niño occurrences have contributed to the diminished maize yields. The objective is to give guidelines to policymakers, researchers, and agricultural stakeholders for taking proactive actions to address the immediate and lasting impacts of El Niño and enhance the resilience of the agricultural industry. This brief paper provides prospective strategies for farmers to anticipate and counteract the El Niño-influenced dry season projected for 2023/24 and beyond. The coefficient of determination R2 between yield and ENSO was low; 11 of the 13 El Niño seasons had a negative detrended yield anomaly, indicating the strong association between El Nino's effects and the reduced maize yields in Zimbabwe. The R2 between the Oceanic Nino Index (ONI) and rainfall (43%) and between rainfall and yield (39%) indirectly affects the association between ONI and yield. To safeguard farmers' livelihoods and improve their preparedness for droughts in future agricultural seasons, this paper proposes a set of strategic, tactical, and operational decision-making guidelines that the agriculture industry should follow. The importance of equipping farmers with weather and climate information and guidance on drought and heat stress was underscored, encompassing strategies such as planting resilient crop varieties, choosing resilient livestock, and implementing adequate fire safety measures. [ABSTRACT FROM AUTHOR]

Accurately identifying and predicting droughts can provide local managers with a basis for decision-making. The Xinyang region is prone to droughts and floods, which have a large impact on local agriculture and socio-economics. This paper employs precipitation data from the Xinyang region to provide a scientific basis for drought and flood control measures in this region. The data are first treated with standardized precipitation indices (SPIs) on three-month, six-month, and nine-month time scales. Subsequently, a Morlet wavelet analysis is performed for each of the three time scales analyzed for the SPI. The results show multiple time scales of drought and flood disasters in the Xinyang region. The cycles of drought and flood disasters in the Xinyang region show different fluctuations on different SPI scales. The SPI time series reflect a strong fluctuation period of 17a for drought and flood disasters in the Xinyang region. An analysis of the variance of the wavelet coefficients showed that the first main cycle of drought and flood disasters in the Xinyang region is 7a, and the second and third sub-cycles are 4a and 13a, respectively. We conclude that floods are more frequent than droughts in Xinyang and are more likely to occur from 2017 to 2021, with a subsequent shift to droughts. Local managers should put drought prevention measures in place to deal with droughts after 2021. [ABSTRACT FROM AUTHOR]

Pakistan is among the countries that are highly vulnerable to climate change. The country has experienced severe floods and droughts during recent decades. The agricultural sector in Pakistan is adversely affected by climate change. This systematic review paper set out to analyze the existing literature on adaptation measures at the farm level toward climate change in Pakistan. Adopting a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method, a total of 62 articles were identified from the Web of Science and Scopus databases. The review paper indicates that the main adaptation strategies adopted by farmers are as follows: changing cropping practices, changing farm management techniques, advanced land use management practices, and nonagriculture livelihood options. Further, this review shows the factors influencing the farmer's adaptation measures to climate change. Influencing factors were examined and classified into three groups: demographic, socioeconomic, and resources and institutional. Barriers hindering farmers' adaptive capacity were identified as lack of access to information and knowledge, lack of access to extension services, lack of access to credit facility, and lack of farm resources. [ABSTRACT FROM AUTHOR]

General circulation models (GCMs) run at regional resolution or at a continental scale. Therefore, these results cannot be used directly for local temperatures and precipitation prediction. Downscaling techniques are required to calibrate GCMs. Statistical downscaling models (SDSM) are the most widely used for bias correction of GCMs. However, few studies have compared SDSM with multi-layer perceptron artificial neural networks and in most of these studies, results indicate that SDSM outperform other approaches. This paper investigates an alternative architecture of neural networks, namely the long-short-term memory (LSTM), to forecast two critical climate variables, namely temperature and precipitation, with an application to five climate gauging stations in the Lake Chad Basin. Lake Chad is a data scarce area which has been impacted by severe drought, where water resources have been influenced by climate change and recent agricultural expansion. SDSM was used as the benchmark in this paper for temperature and precipitation downscaling for monthly time–scales weather prediction, using grid resolution GCM output at a 5 degrees latitude × 5 degrees longitude global grid. Three performance indicators were used in this study, namely the root mean square error (RMSE), to measure the sensitivity of the model to outliers, the mean absolute percentage error (MAPE), to estimate the overall performance of the predictions, as well as the Nash Sutcliffe Efficiency (NSE), which is a standard measure used in the field of climate forecasting. Results on the validation set for SDSM and test set for LSTM indicated that LSTM produced better accuracy on average compared to SDSM. For precipitation forecasting, the average RMSE and MAPE for LSTM were 33.21 mm and 24.82% respectively, while the average RMSE and MAPE for SDSM were 53.32 mm and 34.62% respectively. In terms of three year ahead minimum temperature forecasts, LSTM presents an average RMSE of 4.96 degree celsius and an average MAPE of 27.16%, while SDSM presents an average RMSE of 8.58 degree celsius and an average MAPE of 12.83%. For maximum temperatures forecast, LSTM presents an average RMSE of 4.27 degree celsius and an average MAPE of 11.09 percent, while SDSM presents an average RMSE of 9.93 degree celsius and an average RMSE of 12.07%. Given the results, LSTM may be a suitable alternative approach to downscale global climate simulation models' output, to improve water management and long-term temperature and precipitations forecasting at local level. [ABSTRACT FROM AUTHOR]

Global climate changes, particularly extreme weather events, can directly or indirectly affect freshwater availability and food production, and cause disease outbreaks, floods and droughts. Using the advanced research version of the weather research and forecasting model (ARWv3) and a hydrostatic wind speed change equation, Liu et al. [[5]] assessed the effects of four CPSs on a 10 m wind speed simulation over mainland China in the summer of 2003. It was found that the IAP climate model creditably reproduced the spatial patterns of the first three dominant modes of summer rainfall in Thailand, and that the correlation between the observed rainfall anomalies and the Niño 3.4 index could be reproduced through the use of the IAP model. Advanced climate simulations and observations can improve the accuracy of climate change predictions and long-term trends, which can mitigate the impacts of climate events on social and economic development, as well as human lives. [Extracted from the article]

In the context of anomalous global climate change and the frequent occurrence of droughts and floods, studying trends in the conversion rate between precipitable water vapor (PWV) and actual precipitation in a certain region can help in analyzing the causes of these natural disasters. This paper examines the variation trend in the conversion rate between PWV and actual precipitation on a monthly scale in Hubei from 1960 to 2020. To estimate historical PWV data, we propose a new method for estimating PWV using water vapor pressure based on the RF algorithm. The new method was evaluated by radiosonde data and improved the accuracy by 1 mm over the traditional method in Hubei. Based on this method, we extrapolate the monthly average PWV in Hubei from 1960 to 2020 and analyze the conversion rate between PWV and precipitation during this period. Our results showed that there was no obvious cyclical pattern in the conversion rate in either the longitude or latitude directions. In Hubei, where the topography varies significantly in the longitude direction, the conversion rate is influenced by topography, with the smallest conversion rate being in the transition zone between the mountainous region of western Hubei and the Jianghan Plain. In the latitudinal direction, the conversion rate decreases with increasing latitude. [ABSTRACT FROM AUTHOR]

Drought has a direct impact on regional agricultural production, ecological environment, and economic development. The northwest region of China is an important agricultural production area, but it is also one of the most serious areas of water shortage due to drought and little rain. It is of great significance to make full use of agricultural resources to clarify the temporal and spatial distribution characteristics of the drought regime in Northwest China. Based on the Standardized Precipitation Evapotranspiration Index (SPEI), this paper used the methods of Mann–Kendall non-parameter trend, mutation test, and Morlet wavelet analysis to explore the drought characteristics in Northwest China from 1961 to 2017. The results showed that the spatial distribution of SPEI on annual and seasonal scales differed slightly in different regions, but from northwest to southeast, the distribution was generally wetter to drier. The drought intensity (Sij) had a step-like distribution with a range of 1.14–1.98. Based on Sij analysis, the frequency of drought in Northwest China was moderate, followed by extreme drought, severe drought, and light drought. The inter-annual drought station proportion (Pj) ranged from 7.4% to 84.1%. A total of 25, 18, 7, and 5 years of pan-regional drought, regional drought, partial region drought, and local drought occurred, respectively, based on Pj analysis. Moreover, from the whole study period, the regional drought changes tended to cause humidification to different degrees. The results of Morlet wavelet analysis showed that there were multiple time scales of 33–52, 11–19, and 4–7 years of SPEI in the entire time domain, and dry and wet trends occurred. The results of the present research can provide a reference for the efficient utilization of water resources, drought monitoring and early warning, drought prevention, and drought relief in Northwest China. [ABSTRACT FROM AUTHOR]

Groundwater droughts can explain developments and changes in groundwater from a climatological perspective. The North China Plain (NCP) is a typical underground funnel area. Therefore, groundwater drought studies in the NCP can provide better understanding of the local hydrogeological characteristics from new perspectives. In this paper, the GRACE groundwater drought index (GGDI) was used to evaluate groundwater drought events in the NCP. Additionally, a new method was proposed in this study for investigating groundwater drought events at the spatiotemporal scale. On this basis, the centroid theory was used to construct an appropriate groundwater drought migration model for the NCP. The results showed that (1) the groundwater drought frequency in the NCP was 24.54%. In addition, the most severe groundwater drought events in the study occurred in March 2020. (2) In total, 49 groundwater drought events occurred in the NCP over the 2003–2020 period. The most intense groundwater drought event occurred over the June 2018–December 2020 period (DE.49), covering the entire study area. DE.29 was the second most intense groundwater drought event over the August 2012–September 2013 period (14 months), resulting in a maximum arid area of 75.57% of the entire study area. (3) The migration of the groundwater drought events was in the southwest–northeast and northeast–southwest directions, which was consistent with the terrain inclination, while most of the groundwater drought centroids were concentrated in Area II. The groundwater drought event identification method and the groundwater drought migration model were effective and reliable for assessing groundwater drought events in the NCP and provided a better understanding of developments and changes in groundwater droughts, which is of great practical significance and theoretical value for the rational development and use of groundwater resources, as well as for guiding industrial and agricultural activities. [ABSTRACT FROM AUTHOR]

Damage from climate change is widespread throughout the world. This change has brought about calamities, the most prevalent of which is the emergence of numerous droughts which are increasingly threatening human lives. In this paper, we studied the spatial and temporal variations of drought under the effect of climate change in the Huaibei Plain, which is a very important agricultural zone in China. Drought has attracted increasing attention in research due to its heavy impact on agriculture, the environment, livelihood, and food security. The SPEI (Standardized Precipitation Evapotranspiration Index) has been used in this study to express and identify drought events in the Huaibei Plain due to climate change. A general circulation model (GCM), HadGEM2-AO, which was the most appropriate for the study area's precipitation simulation, and three Representative Concentration Pathways (RCP), RCP 2.6, RCP 4.5, and RCP 8.5, were used to analyze and compare the drought effect for the baseline (1985–2017) and the future climate scenarios (2025–2090). At 3 and 6 months, the SPEI successfully detects agricultural drought in temporal and spatial variation. However, according to the analysis, more severe agricultural drought events are foreseen in the future than in the baseline because of climate change. SPEI performed better than SPI in detecting drought in the baseline and simulated data due to increased evapotranspiration. Between the SPEI-3 and SPEI-6, the Pearson coefficient correlation reveals a positive association. The Mann-Kendall test was used to cover the two studied periods in order to establish the drought trend. Both decreasing and increasing trends, in different timescales, were detected by Sen's Slope in the baseline and future periods with all RCPs. [ABSTRACT FROM AUTHOR]

This paper summarises an updated climate change trends analysis—developed for the period from 1 October 1864 to 30 September 2021 within the scope of a Horizon 2020-funded project to increase climate resilience in European coastal cities—for a representative site of the Lisbon Metropolitan Area (Portugal). By using long ground-based daily records of rainfall and surface temperature at the Lisboa-Geofísico climatological station, the analysis aimed to identify (i) long-term and recent climate trends in rainfall and temperature, (ii) changes in extreme rainfalls, heatwaves, and droughts, and (iii) possible effects of the coupled changes of minimum and maximum daily temperatures (Tmin and Tmax, respectively) on drought development based on the diurnal temperature range (DTR) indicator. To detect these trends and quantify their magnitude, the Mann−Kendall and Sen's slope estimator tests were implemented. The analysis of the mean annual temperatures indicated that the study area has warmed ∼1.91 °C through the 157 analysed years. Results evidenced statistically significant upward trends in both Tmin and Tmax, and in the number of Tmax heatwave days. In what concerns the extreme hydrological events, the analysis of annual maximum rainfall series and peaks-over-threshold (POT) techniques showed more frequent and intense events in recent years, reaching up to ∼120.0 mm in a single day. With regard to drought, the study proved that the characterisation based on the commonly used standardised precipitation index (SPI) might differ from that based on the standardised precipitation evapotranspiration index (SPEI), as the latter can take into account not only rainfall but also temperature, an important trigger for the development of drought. According to the SPEI index, severe and extreme drought conditions have been more frequent in the last 60 years than in any other recorded period. Finally, a decreasing DTR trend towards the present was found to influence evapotranspiration rates and thus drought characteristics. [ABSTRACT FROM AUTHOR]

In recent years, the frequent occurrence of extreme climate disasters has seriously affected agriculture in Northeast China. Based on precipitation data derived from 83 geographical stations in the study area for 60 years (1960–2019), we chose the reliable statistical methods of the Mann–Kendall test, Sen's slope, and the Standardized Precipitation Index, and regarded drought and flood as a whole in this paper, to identify the temporal and spatial variation characteristics of precipitation and extreme climate events in Northeast China. The results show that an increasing trend of disasters was detected at the 95% confidence level (Z = 2.3). According to the timescale analysis, abrupt climate changes started in 2006. Temporal and spatial distribution of extreme climate disasters, mainly drought and flood disasters, showed a significant upward trend from 2006 to 2019. According to the spatial analysis, the precipitation in Northeast China decreased from south to north and fluctuated less from east to west. Moreover, stations with extreme climate trends (trend of climatic anomaly with confidence level > 90%) followed the same spatial pattern as those with a high frequency of extreme climate disasters (more than 17.87 times/decade). The severity and frequency of extreme climate have increasingly threatened Northeast China in the past decade. In particular, the Northeast Plain experienced the most severe and extreme climate events that seriously threatened the study area in 2007, 2009, 2010, and 2019. Our results highlight the urgent need for the development of monitoring and early warning of droughts and flood disasters to reduce economic losses. [ABSTRACT FROM AUTHOR]

Research on precipitation regularity in the past 120 years is an important link in analyzing the precipitation characteristics of watersheds. This paper systematically analyzes the characteristic changes of centennial precipitation data in the Haihe River basin with the help of CRU data, PCI, SPI, and the Pearson type III curve. The results show that the spatial and temporal distribution of precipitation in the Haihe River basin has a more obvious inconsistency. The temporal distribution shows the characteristics of relatively stable in the early period and increasing fluctuation in the later period, the concentration of precipitation gradually decreases, and the overall drought level decreases. The spatial distribution shows a general pattern of gradually decreasing from southwest to northeast, the overall trend of summer precipitation changes from stable to north–south extremes, and the distribution probability of extreme precipitation events in the basin decreases from southeast to northwest, while the drought-prone area transitions from the northeast to the west and southwest of the basin. Under the influence of both climate change and human activities, the seasonal distribution of precipitation tends to be average, the area affected by extreme precipitation rises, and the arid area shifts to the inland area. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) plays an important role in regulating terrestrial–atmospheric water circulation and energy balance. Most of the existing studies have explored the dynamic patterns of SM based on experimental methods. However, the analysis of large-scale regions and long-term SM sequences was limited. Alternatively, satellite remote sensing data is a potential source for SM analysis for large-scale basins. Therefore, the SM data from the European Space Agency (ESA) Climate Change Initiative (CCI) from 2000 to 2015 is used in this paper to analyze the SM spatial-temporal changes in the Yellow River Basin (YRB). Further, the Normalized Difference Vegetation Index (NDVI) and meteorological data are used to explore the relationships between SM and NDVI, precipitation, air temperature, and wind speed, respectively. The results showed that the overall trend of SM in the YRB was decreasing from southeast to northwest during the past 16 years. The upper reaches of the YRB had shown a humid trend, with a value of 0.00047 m3·m−3·year−1, mainly due to the increase in precipitation; there was an obvious drought trend in the middle reaches of the YRB, especially in Shanxi Province and Henan Province, with a value of −0.00030 m3·m−3·year−1, which may be owed to vegetation greening increasing the soil evaporation. Overall, it is determined that the main factors influencing SM changes were NDVI and precipitation, followed by air temperature and wind speed. This study can provide a scientific basis for the spatial-temporal distribution characteristics and attributions of SM in the YRB over a long time series. [ABSTRACT FROM AUTHOR]

The socioeconomic impacts caused by floods in the south of Eastern Siberia (SES), and the expected increase in precipitation extremes over northern Eurasia, have revealed the need to search for atmospheric circulation patterns that cause extreme precipitation events (EPE) in SES, as well as their changes. We investigate the circulation patterns causing extreme precipitation in SES and Mongolia, by examining the instability and moisture transport associated with potential vorticity (PV) dynamics during two time periods: 1982–1998 and 1999–2019. The EPE were characterized by an increase in instability within the precipitation area, which was compensated by stability around the area, with the East Asian summer monsoon transport being enhanced. PV in the subtropical regions and mid-latitudes has shown the amplification of positive and negative PV anomalies to the southeast and northwest of Lake Baikal, respectively. The PV contours for EPE have shapes of cyclonic wave breaking and cutoff low. EPE accompanied by wave breaking are characterized by strong redistribution areas, with extremely high and low stability and moisture. This can lead to the coexistence of floods and droughts, and in part was the driver of the earlier revealed "seesaw" precipitation mode over Mongolia and SES. We suggest a shift of extreme precipitation to the northwest has occurred, which was probably caused by the wave propagation change. [ABSTRACT FROM AUTHOR]

Future climate change and its impact on drought is critical for Uzbekistan, located in Central Asia, the world's largest arid zone. This study examines the evolving intensity of climate change and drought events using multi-model ensembles (MMEs) derived from the Coupled Model Intercomparison Project Phase 5 and 6 (CMIP5 and CMIP6) simulated under the Representative Concentration Pathway and Shared Socioeconomic Pathway (RCP and SSP) scenarios. The projections show different rates of increase in temperature and precipitation under the RCPs and SSPs. Projected temperature increases are expected to reach up to 2–2.5 °C under SSP1-2.6, SSP2-4.5, and SSP3-7.0, by mid-century. By 2080–2099, an increase is projected of 2–3 °C in monthly mean temperatures throughout the year (SSP1-2.6), and a more pronounced increase in summer up to 3–4 °C (SSP2-4.5) and 4–6 °C (SSP3-7.0), with a marked contrast in conditions between the mountainous and desert regions of Uzbekistan. Regional changes in precipitation over the study periods show relatively little variability, except for FD, where notable trends are found. Under SSP1-2.6 and SSP2-4.5, the increase in precipitation is relatively modest, whereas the changes in SSP3-7.0 are more substantial, with some regions experiencing variations of up to 10–20 mm per period. The Standardized Precipitation Evapotranspiration Index (SPEI), calculated based on the projected temperature and precipitation, provides an estimate of future drought trends. Our results show increasing aridity under all scenarios by mid-century, with longer-term projections indicating stabilization around different SPEI values by 2100: RCP2.6 and SSP1-1.9 stabilize around −1.0; RCP4.5, RCP6.0, SSP2-4.5, and SSP3-7.0 stabilize around −1.5; while RCP8.5 and SSP5-8.5 scenarios project values of −2 or less by 2100. Notable differences in the SPEI index are found between lowland and foothill regions. In view of Uzbekistan's heavy reliance on agriculture and irrigation, which are the sectors that are expected to be mostly affected by climate change, our study provides a scientific basis for informed policy decision-making. This includes various aspects such as planning and management water resources, as well as the broader socioeconomic development of the country. [ABSTRACT FROM AUTHOR]

Flash droughts (FDs) pose significant challenges for accurate detection due to their short duration. Conventional drought monitoring methods have difficultly capturing this rapidly intensifying phenomenon accurately. Machine learning models are increasingly useful for detecting droughts after training the models with data. Northeastern Brazil (NEB) has been a hot spot for FD events with significant ecological damage in recent years. This research introduces a novel 2D convolutional neural network (CNN) designed to identify spatial FDs in historical simulations based on multiple environmental factors and thresholds as inputs. Our model, trained with hydro-climatic data, provides a probabilistic drought detection map across northeastern Brazil (NEB) in 2012 as its output. Additionally, we examine future changes in FDs using the Coupled Model Intercomparison Project Phase 6 (CMIP6) driven by outputs from Shared Socioeconomic Pathways (SSPs) under the SSP5-8.5 scenario of 2024–2050. Our results demonstrate that the proposed spatial FD-detecting model based on 2D CNN architecture and the methodology for robust learning show promise for regional comprehensive FD monitoring. Finally, considerable spatial variability of FDs across NEB was observed during 2012 and 2024–2050, which was particularly evident in the São Francisco River Basin. This research significantly contributes to advancing our understanding of flash droughts, offering critical insights for informed water resource management and bolstering resilience against the impacts of flash droughts. [ABSTRACT FROM AUTHOR]

It is important to consider extreme climate events, reduce disaster losses, and formulate effective disaster prevention and mitigation countermeasures. Based on the daily data from 36 meteorological stations of Xinjiang from 1960 to 2020, in order to analyze the temporal–spatial variations and influencing factors of extreme dry and wet events in Xinjiang, a number of methods were used including climate trend, the Mann–Kendall test, the Fourier power spectrum, the contribution rate, partial least squares and cross-wavelet analysis. Results indicate that the annual average frequency of extreme dry/wet events has a decreasing/increasing trend, at the rate of 0.26 times/decade and 0.19 times/decade, respectively; the variation trend in extreme dry and wet events of four seasons are consistent with the annual counterpart, at the rate of −0.04 times/decade and 0.02 times/decade (spring), −0.08 times/decade and 0.05 times/decade (summer), −0.05 times/decade and 0.06 times/decade (autumn), and −0.1 times/decade and 0.08 times/decade (winter). Fe fluctuation is greatest in winter and the smallest in spring, so the transition to warm and wet is obvious in winter and spring drought is easy to occur; the variation extent of extreme dry and wet events in northern Xinjiang exceeds the counterpart in southern Xinjiang; 1986 and 1987 witnessed abrupt variation in extreme dry and wet events in Xinjiang, with indication of distinct periodic oscillations of 2.44, 2.94, and 5.69 years and 2.94 and 5.69 years, respectively; the extreme dry (wet) events are determined by meteorological factors, comprising precipitation, relative humidity and temperature, and the circulation factors constituted by Western Pacific Subtropical High-Intensity Area (East Asian Trough Intensity, Westerly Circulation and Western Pacific Subtropical High Area) and El Niño events. [ABSTRACT FROM AUTHOR]

This paper presents the results of research on possible climate change in Bosnia and Herzegovina according to the climate scenario RCP8.5 and its potential impact on fruit production. Climate change analyses are based on expected fluctuations in air temperature, precipitation and climate indices. The results indicate pronounced climate change, which refers to an increase in annual temperature to 5 °C, and a decrease in annual precipitation of up to 30% and in the summer season (June, July, and August) and up to 40% by the end of the XXI century. In addition, an increase in the number of summer days and a decrease in the number of days with the appearance of snow can be expected. Reducing the number of days with snow and snow cover can cause a decrease in underground aquifers with water during the winter and spring seasons. These changes can have a serious impact on the problem of drought and water deficit, which can have direct consequences for the agricultural sector in Bosnia and Herzegovina, especially for fruit production. These findings show that fundamental changes in agriculture and an approach to land treatment and water resources management, as well as fruit production planning in changed climatic conditions, are needed. [ABSTRACT FROM AUTHOR]

(1) Introduction: The subject of the present study concerns the analysis of the existence and long time evolution of the solar ~200 yr (de Vries/Suess) and ~2400 yr (Hallstadt) cycles during the recent part of the Wurm ice epoch and the Holocene, as well as their forcing on the regional East European climate during the last two calendar millennia. The results obtained here are compared with those from our previous studies, as well as with the results obtained by other authors and with other types of data. A possible scenario of solar activity changes during the 21st century, as well as different possible mechanisms of solar–climatic relationships, is discussed. (2) Data and methods: Two types of indirect (historical) data series for solar activity were used: (a) the international radiocarbon tree ring series (INTCAL13) for the last 13,900 years; (b) the Schove series of the calendar years of minima and maxima and the magnitudes of 156 quasi 11 yr sunspot Schwabe–Wolf cycles since 296 AD and up to the sunspot cycle with number 24 (SC24) in the Zurich series; (c) manuscript messages about extreme meteorological and climatic events (Danube and Black Sea near-coast water freezing), extreme summer droughts, etc., in Bulgaria and adjacent territories since 296 and up to 1899 AD, when the Bulgarian meteorological dataset was started. A time series analysis and χ2-test were used. (3) Results and analysis: The amplitude modulation of the 200 yr solar cycle by the 2400 yr (Hallstadt) cycle was confirmed. Two groups of extremely cold winters (ECWs) during the last ~1700 years were established. Both groups without exclusion are concentrated near 11 yr sunspot cycle extremes. The number of ECWs near sunspot cycle minima is about 2 times greater than that of ECWs near sunspot cycle maxima. This result is in agreement with our earlier studies for the instrumental epoch since 1899 AD. The driest "spring-summer-early autumn" seasons in Bulgaria and adjacent territories occur near the initial and middle phases of the grand solar minima of the Oort–Dalton type, which relate to the downward phases and minima of the 200 yr Suess cycle. (4) Discussion: The above results confirm the effect of the Sun's forcing on climate. However, it cannot be explained by the standard hypothesis for total solar irradiation (TSI) variations. That is why another hypothesis is suggested by the author. The mechanism considered by Svensmark for galactic cosmic ray (GCR) forcing on aerosol nuclei was taken into account. However, in the hypothesis suggested here, the forcing of solar X-ray flux changes (including solar flares) on the low ionosphere (the D-layer) and following interactions with the Earth's lithosphere due to the terrestrial electric current systems play a key role for aerosol nuclei and cloud generation and dynamics during sunspot maxima epochs. The GCR flux maximum absorption layer at heights of 35–40 km replaces the ionosphere D-layer role during the sunspot minima epochs. [ABSTRACT FROM AUTHOR]

To ensure water use and water resource security along "the Belt and Road", the runoff and hydrological droughts and floods under future climate change conditions in the upper Heihe River Basin were projected in this study, based on the observed meteorological and runoff data from 1987 to 2014, and data from 10 GCMs from 1987 to 2014 and from 2026 to 2100, using the SWAT model, the Standardized Runoff Index, run length theory, and the entropy-weighted TOPSIS method. Both the multi-GCM ensemble (MME) and the optimal model were used to assess future hydrological drought and flood responses to climate change. The results showed that (1) the future multi-year average runoff from the MME was projected to be close to the historical period under the SSP245 scenario and to increase by 2.3% under the SSP585 scenario, and those from the optimal model CMCC-ESM2 were projected to decrease under both scenarios; (2) both the MME and the optimal model showed that drought duration and flood intensity in the future were projected to decrease, while drought intensity, drought peak, flood duration, and flood peak were projected to increase under both scenarios in their multi-year average levels; (3) drought duration was projected to decrease most after 2080, and drought intensity, flood duration, and flood peak were projected to increase most after 2080, according to the MME. The MME and the optimal model reached a consensus on the sign of hydrological extreme characteristic responses to climate change, but showed differences in the magnitude of trends. [ABSTRACT FROM AUTHOR]

High-temperature and drought events significantly impact crop growth and development. In the soybean-producing region of the Guanzhong Plain in China, understanding the dynamics of these climatic phenomena is vital for soybean yield preservation. Through a fixed-position observation experiment that analyzed four growth stages, nine agronomic traits, and soybean yield per unit area from 1998 to 2023, this research evaluated the characteristics of high-temperature and drought processes in various growth stages. It also examined the influence of high-temperature processes, drought processes, and their combined effects on agronomic traits and yield. The results indicate the following: (1) High temperature was a constant factor during the soybean growth period, with temperature-related indices markedly surpassing those related to drought. Notably, the occurrence of high-temperature and drought events was more prevalent during the flowering–podding stage than at the podding or grain-filling stages. (2) High temperature profoundly affected soybean yield components, primarily through a decrease in the number of grains per plant during the flowering–podding stage, subsequently impacting the grain weight per plant and yield. In years with extremely high temperatures, the soybean plant height was reduced by 6.1 to 15 cm, the main stem node number decreased by 0.1 to 2.9, the branch number decreased by 0.2 to 0.6, the number of pods per plant decreased by 4.8 to 13.7, the number of grains per pod decreased by 0.1 to 0.3, the number of grains per plant decreased by 13.5 to 32.6, the grain weight per plant decreased by 3.8 to 6.9 g, and the 100-grain weight decreased by 0.1 to 4.5 g. The common impact of high temperature combined with drought processes in different growth stages was reflected in the reduction in the number of branches by 0.1 to 1.4 and the reduction in the number of grains per pod by 0.02 to 13.7. This study underscores the importance of addressing the quantitative effects of climate change and extreme weather on soybean yield, which could help to develop effective adaptation and mitigation strategies. [ABSTRACT FROM AUTHOR]

In recent years, extreme hydro-climate events (such as floods and droughts) have occurred more frequently, leading to significant threats to lives and damage of property [[1], [3], [5]]. This Special Issue (SI) was proposed with the aim to collect the latest methodological developments and applications in studying both historic and future extreme hydro-climate events. 2020; 29: 100265 3 Liu S.N., Shi H.Y., Sivakumar B. Socioeconomic drought under growing population and changing climate: A new index considering the resilience of a regional water resources system. [Extracted from the article]

Rural communities in West Africa have long adopted a variety of coping and adaptation strategies to periods of climate variability and risks. These strategies have mostly been shaped by prevailing indigenous knowledge systems and shared understandings of the underlying causes of climate events. Despite the increasing scientific and policy attention to climate perceptions and integration of indigenous knowledge in climate governance, there is still a lag in going further to probe and consider the socio-cultural and cognitive systems that shape local appreciation of climate change risks and responses. Based largely on qualitative interviews, and complementary household surveys, the paper draws on the concepts of 'mental' and 'cognised' models to examine drought and climate change risk perceptions and responses in the rural savannah of North-eastern Ghana. Local farmers generally allude to changes in rainfall patterns and prolonged intra-seasonal dry spells. Based on subscriptions to local models of blame in explaining climate risks and impacts, it is also seen that prevailing socio-cultural beliefs and understandings of environmental events tend to inform the responses of farmers in addressing these perceived risks and impacts. The paper advocates for ongoing climate action and policy processes to consider the complexity of different actors and context (socio-cultural, institutional, power structures) in enhancing sustainable adaptation and mitigation measures in vulnerable rural communities. [ABSTRACT FROM AUTHOR]

Drought is a natural disaster that occurs globally and can damage the environment, disrupt agricultural production and cause large economic losses. The accurate prediction of drought can effectively reduce the impacts of droughts. Deep learning methods have shown promise in drought prediction, with convolutional neural networks (CNNs) being particularly effective in handling spatial information. In this study, we employed a deep learning approach to predict drought in the Fenhe River (FHR) basin, taking into account the meteorological conditions of surrounding regions. We used the daily SAPEI (Standardized Antecedent Precipitation Evapotranspiration Index) as the drought evaluation index. Our results demonstrate the effectiveness of the CNN model in predicting drought events 1~10 days in advance. We evaluated the predictions made by the model; the average Nash–Sutcliffe efficiency (NSE) between the predicted and true values for the next 10 days was 0.71. While the prediction accuracy slightly decreased with longer prediction lengths, the model remained stable and effective in predicting heavy drought events that are typically difficult to predict. Additionally, key meteorological variables for drought predictions were identified, and we found that training the CNN model with these key variables led to higher prediction accuracy than training it with all variables. This study approves an effective deep learning approach for daily drought prediction, particularly when considering the meteorological conditions of surrounding regions. [ABSTRACT FROM AUTHOR]

The Lancang-Mekong River Basin (LMRB) is one of the major transboundary basins globally, facing ongoing challenges due to flood and drought disasters. Particularly in the past two decades, the basin has experienced an increased frequency of meteorological drought events, posing serious threats to the local socio-economic structures and ecological systems. Thus, this study aimed to analyze the meteorological drought characteristics in the LMRB and identify the impact and correlation of atmospheric circulation on the meteorological drought in the basin. Specifically, the different levels of meteorological drought events were defined using the Run Theory based on the seasonal and annual SPEI from 1980 to 2018. The time lag correlation between meteorological drought events and the EI Nino-Southern Oscillation (ENSO), Arctic Oscillation (AO), North Atlantic Oscillation (NAO), and Pacific Decadal Oscillation (PDO), were analyzed in the LMRB. Our results indicated that, from a temporal perspective, the period from November to April of the following year was particularly prone to meteorological droughts in the basin. In terms of spatial distribution, the primary agricultural regions within the basin, including Thailand, Eastern Cambodia, and Vietnam, were highly susceptible to meteorological droughts. Further analysis revealed a teleconnection between drought events in the LMRB and atmospheric circulation factors. The sensitivity of the basin's drought timing to its response decreased in the order of the ENSO > AO > NAO > PDO. In general, the ENSO had the most substantial influence on drought events in the basin, with the strongest response relationship, while the upper reaches of the basin displayed the most significant response to the AO; the occurrence and progression of meteorological droughts in this area synchronized with the AO. These findings enhance our understanding of drought-prone areas in the LMRB, including the meteorological factors and driving mechanisms involved. This information is valuable for effectively mitigating and managing drought risks in the region. [ABSTRACT FROM AUTHOR]

Recent studies on China's arid and semi-arid regions, particularly the Tarim River Basin (TRB), have shown an increase in the intensity and frequency of extreme weather events. This research examines the link between meteorological droughts, as measured by the Standardized Precipitation Evapotranspiration Index (SPEI), and hydrological droughts, as indicated by the Standardized Runoff Index (SRI) and the Standardized Terrestrial Water Storage Index (STI), over various time scales. Historical data indicate that SPEI drought frequency (DF) was 14.3–21.9%, with prevalent events in the northern oases. SRI DF ranged from 9.0% to 35.8%, concentrated around the Taklamakan and Kumtag Deserts, while STI DF varied between 4.4% and 32.7%, averaging 15% basin-wide. Future projections show an increased DF of SPEI in deserts and a decrease in oases; SRI DF decreased in deserts but increased in oases. STI changes were more moderate. The study also found a higher risk of drought progression from SPEI to SRI in the southwestern and northeastern oases, exceeding 50% probability, while central and eastern TRB had lower risks. The western TRB and inner Taklamakan Desert faced higher risks of SPEI to STI progression, with probabilities over 45%, in contrast to the lower risks in the eastern and central oases. The concurrence of SRI/STI with moderate to extreme SPEI droughts led to a higher probability and area of SRI/STI droughts, whereas consistent SPEI types showed a reduced induced probability and extent of SRI/STI droughts. This study enhances the understanding of drought propagation from meteorological to hydrological droughts in the TRB and contributes to the prevention of hydrological drought to a certain extent. [ABSTRACT FROM AUTHOR]

Understanding and projecting drought, especially in the face of climate change, is crucial for assessing its impending risks. However, the causes of drought are multifaceted. As the environmental research paradigm pivots towards machine learning (ML) for predictions, our investigation contrasted multiple ML techniques to simulate the Standardized Precipitation Evapotranspiration Index (SPEI) from 2009 to 2022, utilizing various potential evapotranspiration (PET) methods. Our primary focus was Australia, the world's driest inhabited continent. Given the challenges with ML model interpretation, SHAP (SHapley Additive exPlanations) values were employed to decipher SPEI variations and to gauge the relative importance of precipitation (Prec) and PET in six key Australian cities. Our findings revealed that while different PET methods resulted in distinct mean values, their trends remained consistent. Post the Millennium Drought, Australia experienced several drought events. SPEI discrepancies based on PET methods were minimal in humid regions like Brisbane and Darwin. However, for arid cities, the Priestley–Taylor equation-driven SPEI differed notably from other methods. Ridge regression was the most adept at mirroring SPEI changes among the assessed ML models. Furthermore, the SHAP explainer discerned that PET-related climate variables had a greater impact on SPEI in drier cities, whereas in humid cities, Prec was more influential. Notably, the research emphasised CO2′s role in influencing drought dynamics in humid cities. These insights are invaluable for enhancing drought mitigation strategies and refining predictive models. Such revelations are crucial for stakeholders aiming to improve drought prediction and management, especially in drought-prone regions like Australia. [ABSTRACT FROM AUTHOR]

Understanding the spatiotemporal distribution of extreme rainfall and meteorological drought on a watershed scale could be beneficial for local management of any water resources system that supports dam operation and river conservation. This study considered the watershed of Angat as a case, given its economic importance in the Philippines. A series of homogeneity tests were initially conducted on each rainfall dataset from monitoring stations in and near the watershed, followed by trend analysis to determine the rate and direction of change in the annual and seasonal rainfall extreme indices in terms of intensity, duration, and frequency. Three indices, using the rainfall deviation method (%DEV), percent of normal rainfall index (PNRI), and Standardized Precipitation Index (SPI), were also used to identify meteorological drought events. Generally, rainfall in the watershed has an increasing annual PCPTOT (4–32 mm/year), with increasing frequency and intensity in heavy rainfall and wet days. A significant increasing trend (α = 5%) in the seasonal PCPTOT (7–65 mm/year) and R10mm (1.7–10.0 days/decade) was particularly observed in all stations during the Amihan Monsoon Season (Dec–Feb). The observed increasing rainfall intensity and frequency, if it continues in the future, could have an implication both for the water resources operation to satisfy the multiple objectives of Angat Reservoir and for the flood operation that prevents damage in the downstream areas. The effect of each ENSO (El Niño- Southern Oscillation) phase on the rainfall is unique in magnitude, intensity, and duration. The seasonal reversal of the ENSO in the extreme rainfall and meteorological drought signals in Angat Watershed was also evident. The identified meteorological drought events in the watershed based on SPI-12 persisted up to 12–33 months, could reduce more than 60% (PNRI < 40%) of the normal rainfall. Insights from the study have implications for the hydrology of the watershed that should be considered for the water resources management of the Angat Reservoir. [ABSTRACT FROM AUTHOR]

The primary innovation of this study lies in the development of an integrated modeling framework that combines downscaled climate projections, land-use-change simulations, and copula-based risk analysis. This framework allows for the assessment of localized sub-seasonal and seasonal drought hazards under future scenarios. The BCC-CSM1-1 climate model projections from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset are utilized to represent the future climate for 2025–2060 under RCP 4.5 and 8.5 scenarios. The CA-Markov model is employed to predict future land-use-change distributions. The climate–land use–drought modeling nexus enables the generation of refined spatio-temporal projections of meteorological and hydrological drought risks in the Yellow River Basin (YRB) in the future period of 2025–2060. The results highlight the increased vulnerability of the upper YRB to sub-seasonal meteorological droughts, as well as the heightened sub-seasonal hydrological drought risks in the Loess Plateau. Furthermore, downstream areas experience escalated seasonal hydrological drought exposure due to urbanization. By providing actionable insights into localized future drought patterns, this integrated assessment approach advances preparedness and climate adaptation strategies. The findings of the study enhance our understanding of potential changes in this integral system under the combined pressures of global climate change and land use shifts. [ABSTRACT FROM AUTHOR]

This study analyzes the potential response of the seasonal cycle of heatwaves (HWDI) and dry (CDD) and wet (CWD) spell indices over West Africa for the near- (2031–2060) and the far-future periods (2071–2100) under RCP4.5 and RCP8.5 scenarios using Coordinated Regional Climate Downscaling Experiment (CORDEX) simulations. Despite the fact that some relative biases (an underestimation of 30% for CDD, an overestimation of about 60% for CWD, and an overestimation of about 50% for HWDI) exist, during the historical period (1976–2005) in general, the CORDEX simulations and their ensemble mean outperform the seasonal variability in the above-mentioned indices over three defined subregions of West Africa (i.e., the Gulf of Guinea and Western and Eastern Sahel). They show high correlation coefficients (0.9 in general) and less RMSE. They project an increase (about 10 and 20 days) in heatwave days for both the near- and far-future periods over the whole West African region under both RCP scenarios. In addition, projections indicate that the Sahel regions will experience a decrease (about 5 days) in wet spell days from March to November, while in the Gulf of Guinea, a decrease (about 3 days) is projected throughout the year, except in the CCCLM simulation, which indicates an increase (about 5 days) during the retreat phase of the monsoon (October to December). Our results also highlight an increase (about 80%) in dry spells over the Sahel regions that are more pronounced during the March–November period, while over the Gulf of Guinea, an increase (about 40%) is projected over the entire year. Moreover, the months of increasing dry spells and decreasing wet spells coincide, suggesting that countries in these regions could be simultaneously exposed to dry seasons associated with a high risk of drought and heatwaves under future climate conditions. [ABSTRACT FROM AUTHOR]

Drought is the main meteorological disaster that affects the yield and quality of spring maize on the Loess Plateau. This study used data of the spring maize growth period, relative soil humidity, and yield at 18 agricultural meteorological observation stations on the Loess Plateau from 1997 to 2013 to determine the drought category based on the yield reduction rate. Through the drought index according to the conformity rate of category standard and individual case verification, a refined suitability drought index of spring maize on the Loess Plateau was constructed, and the spatial distribution characteristics of drought in different growth stages of spring maize were analyzed. The results showed the following: (1) The number of days in the whole growth period of spring maize in all regions of the Loess Plateau has been extended. The average sowing date of spring maize in the northwest region of the Loess Plateau was 9 April, and that in the east and central regions was 26 April. In terms of spatial distribution, each growth period was gradually delayed from west to east. (2) The correlation between relative soil humidity and yield of spring maize at the jointing stage and heading stage was the best, followed by the milky stage and mature stage, and the relative soil humidity at the sowing stage and emergence stage had little effect on the yield. (3) According to the national drought category standard "Drought Category of Spring Maize in the North", based on the data of yield reduction rate, the drought index of spring maize on the Loess Plateau was refined by region and growth stage. The drought category index values of spring maize in different growth stages and regions changed according to the revised drought category standard, with 71.4% of the sites in the sowing seedling stage and 85.7% of the sites in the seedling jointing stage, and the revised drought category was more severe than the national drought category standard, while at 57.1% of the sites in the jointing and tasseling stages and 71.4% in the tasseling and milking stages, the revised drought category was less severe than the national drought category standard. (4) Based on the revised refined drought index for spring maize on the Loess Plateau, the spatial distribution of drought occurrence frequency across different growth stages of spring maize on the Loess Plateau was analyzed. The frequency of drought occurrence during the seeding and emergence stages was 25–75%. With the change in growth stages, the high-value area of drought occurrence frequency gradually moved northward, and the overall frequency of drought occurrence decreased. For the milky mature stage, the frequency of drought occurrence in a few regions was around 42%, and the drought frequency in most regions was between 8% and 33%. [ABSTRACT FROM AUTHOR]

Mountains are highly sensitive to the effects of climate change, including extreme short- and long-term weather phenomena. Therefore, in spite of relatively high annual precipitation totals, mountains might become endangered by droughts. The paper presents drought trends in the Polish Carpathians located in Central Europe. Data from the period 1991–2020 from 12 meteorological stations located in various vertical climate zones of the mountains were used to define drought conditions using the following indices: Standardized Precipitation (SPI), Standardized Precipitation Evapotranspiration (SPEI), Relative Precipitation (RPI) and Sielianinov. Additionally, four forest drought indices were used in order to estimate the impact of drought on beech as a typical Carpathian tree species, i.e., the Ellenberg (EQ), Forestry Aridity (FAI), Mayr Tetratherm (MT) and De Martonne Aridity (AI) indices. Statistically significant but weak trends were obtained for the 6-month SPI for four stations (indicating an increase in seasonal to mid-term precipitation), for the 1-month SPEI for three stations, for the 3-month SPEI for four stations, and for MT for all stations (indicating an increase in drought intensity). The analysis of dry month frequency according to particular indices shows that at most of the stations during the last decade of the study period, the frequency of dry months was much higher than in previous decades, especially in the cold half-year. Two zones of the Polish Carpathians are the most prone to drought occurrence: the peak zone due to the shift in climatic vertical zones triggered by the air temperature increase, and the forelands and foothills, together with basins located about 200–400 m a.s.l., where the mean annual air temperature is the highest in all the vertical profile, the annual sums of precipitation are very diversified, and the conditions for beech are already unfavorable. [ABSTRACT FROM AUTHOR]

Drought is one of the most severe climatic calamities, affecting many aspects of the environment and human existence. Effective planning and decision making in disaster-prone areas require accurate and reliable drought predictions globally. The selection of an effective forecasting model is still challenging due to the lack of information on model performance, even though data-driven models have been widely employed to anticipate droughts. Therefore, this study investigated the application of simple extreme learning machine (ELM) and wavelet-based ELM (W-ELM) algorithms in drought forecasting. Standardized runoff index was used to model hydrological drought at different timescales (1-, 3-, 6-, 9-, and 12-month) at five Wadi Mina Basin (Algeria) hydrological stations. A partial autocorrelation function was adopted to select lagged input combinations for drought prediction. The results suggested that both algorithms predict hydrological drought well. Still, the performance of W-ELM remained superior at most of the hydrological stations with an average coefficient of determination = 0.74, root mean square error = 0.36, and mean absolute error = 0.43. It was also observed that the performance of the models in predicting drought at the 12-month timescale was higher than at the 1-month timescale. The proposed hybrid approach combined ELM's fast-learning ability and discrete wavelet transform's ability to decompose into different frequency bands, producing promising outputs in hydrological droughts. The findings indicated that the W-ELM model can be used for reliable drought predictions in Algeria. [ABSTRACT FROM AUTHOR]

The summer of 2022 was very dry across Missouri and the surrounding regions including much of the Great Lakes, Midwest, and southern plains of the USA. A comparison of this summer to the dry summer of 2012 and the relatively wet summers of 2018 and 2021 was carried out using the National Centers for Environmental Prediction/National Centers for Atmospheric Research reanalysis, the Climate Prediction Center teleconnection indexes, and the blocking archive at the University of Missouri. The summer of 2022 was like that of 2012 which was characterized by a strong 500 hPa height anomaly centered over the western US and plains as well as very little blocking in the East Pacific. The summers of 2018 and 2021 were characterized by more zonal flow over the USA and more blocking in the East Pacific, similarly to the results of an earlier study. The teleconnection indexes for the prior spring and summer were largely similar for the two drier years and opposite for the wetter years. The surface conditions for the drier years were more similar while these were opposite for the wetter years. The integrated enstrophy (IE) used in earlier studies identified a change in the large-scale flow regime in early June 2022, which coincided with a decrease in the precipitation over the study region. However, one key difference was that the spring of 2022 was characterized by blocking more consistent with a wetter summer. This would have made the predictability of the drought of summer 2022 less certain. [ABSTRACT FROM AUTHOR]

Vegetation, especially forest ecosystems, plays an important role in the global energy flow and material cycle. The vegetation index (VI) is an important index reflecting the dynamic change in vegetation and directly reflects the response of ecosystem to global climate change. The Greater Khingan Mountains Forest region is located in the northeast of China. It is the largest primeval forest region in China, which is well preserved and less affected by human activities. It is of great significance to study the driving mechanism of forest vegetation change for future ecological prediction and management. In this study, GIMMS NDVI data were used to explore the characteristics of nonlinear temporal and spatial variation of NDVI in the Greater Khingan Mountains and its relationship with climatic factors. Firstly, the EEMD method was used to analyze the characteristics of vegetation change in the study area from 1982 to 2015. Secondly, the relationship between vegetation change and climate was discussed by using precipitation and temperature data. The results showed that the following: (1) from 1982 to 2015, the interannual change in vegetation in the Greater Khingan Mountains presented a trend of slow fluctuation and gradual decrease (SLOPE = −0.1645/10,000, p < 0.01). (2) The spatial distribution of vegetation change had obvious geographical differences, and in the central region, the overall distribution characteristics had an obvious browning trend, and in the northwest and southeast, the distribution characteristics had a green trend. (3) The correlation analysis results of vegetation change and climate factors showed that NDVI change was significantly positively correlated with temperature and precipitation; additionally, NDVI change was more correlated with temperature with a range of 0.8–1 than precipitation. (4) The results of vegetation attribution analysis in four typical areas of the study area showed that the following: the coniferous forest area has good cold tolerance and drought tolerance, the correlation between vegetation change and climate factors (temperature, precipitation) was not the strongest, which was 0.537 and 0.828, respectively. The ecological transition area and the broad-leaved forest area, which was located at the edge of the study area, have relatively fragile ecosystems, showed a strong correlation with precipitation, and the correlation coefficients reached 0.670 and 0.632, respectively. The surface water resources provide favorable conditions for the growth of vegetation, it showed a weak correlation with precipitation, and the correlation coefficient was 0.5349. [ABSTRACT FROM AUTHOR]

Co-occurring extreme heat, drought, and moisture events are increasing under global warming and pose serious threats to ecosystem and food security. However, how to effectively link compound agrometeorological disasters (CADs) with climate change has not been well assessed. In this study, we focus on the comprehensive influence of large-scale climate factors on CADs rather than extreme meteorological elements. The results indicate that there are two main CADs of spring maize in Shenyang, Northeast China (NEC), including concurrent drought and cold damage (DC) and drought in multiple growth periods (MD). The related circulation anomalies at mid–high latitudes are identified as four patterns, namely, the Northeast Asia Low (NEAL) and Ural High (UH) patterns affecting DC, the Baikal High and Okhotsk Low (BHOL), and the Northeast Asia High (NEAH) patterns leading to MD. The vertical profile and water vapor transport anomalies further demonstrate the influence mechanism of large-scale circulation on compound heat–moisture stresses. This study highlights the role of atmospheric circulation, which can provide effective predictors for these synergistic agrometeorological disasters. [ABSTRACT FROM AUTHOR]

Global drought patterns are substantially impacted by climate change, with far-reaching implications for socioeconomic and ecological systems. Existing global climate models (GCMs) are unable to accurately project precipitation and drought characteristics, particularly in countries or regions with complex topography and significant seasonal variability, such as China. Consequently, the purpose of this study is to assess the efficacy of GCMs, and their multi-model ensemble mean, as well as to investigate the seasonal drought characteristics in China using precipitation data from CMIP6 under various "possible future" scenarios. This study selected five GCMs with historical (1961–2014) and future (2015–2100) periods, namely CNRM-CM6-1, GFDL-ESM4, MPI-ESM1-2-HR, MPI-ESM1-2-LR, and NorESM2-MM, as well as their ensemble mean ENS-CGMMN. Based on the China Daily Precipitation Analysis Product (CPAP) as the reference precipitation, the performance of these models is evaluated using the DISO index and the quantile mapping (QM) method for calibration, as well as seasonal-scale drought using the standardized precipitation index (SPI) and spatiotemporal variability analysis methods. In comparison to other climate models and the ensemble mean, the calibrated MPI-ESM1-2-HR model can more precisely describe the actual precipitation conditions at the seasonal scale. Under four scenarios, China's climate will shift from arid to moist in the future period (2015–2100) (SSP126, SSP245, SSP370, and SSP585). Autumn and summer will see a considerable increase in China's moisture levels. During the autumn, winter, and spring, the moisture will generally increase in the northern subregions of China, including the Qinghai-Tibet Plateau (QTP), Xinjiang (XJ), Northwest (NW), Northeast (NE), and North China (NC). Dryness will decrease in southern subregions, such as the Southwest (SW) and South China (SC). In contrast to these three seasons, summer in XJ exhibits a distinct trend of aridity, especially in the SSP245 scenario, whereas the NE, NC, and SC exhibit a distinct trend of moisture. To be more specific, the aridity changes in subregions during various seasons under different future climate scenarios vary significantly. This study's findings can provide significant support for future research on climate change and drought, which can help improve the accuracy of future climate projections and serve as a reference for drought risk management and policy formulation. [ABSTRACT FROM AUTHOR]

A method was put forward to identify the combined heat and drought (CHD) events that occurred in summer and affected spring maize in Liaoning province. The spatial and temporal characteristics of CHD and its effects on maize were evaluated based on daily meteorological data at 52 meteorological stations in Liaoning from 1961 to 2020, as well as agricultural data including details of the maize development periods. The effects of CHD on the photosynthetic capacity of maize were evaluated using SIF remote sensing data from 2001 to 2020. The differences in maize photosynthetic capacity in the summers of 2009 and 2018 were compared in detail. The results show that from 1961 to 2020, the occurrence range, frequency, and severity of summer CHD events increased in Liaoning. CHD events were more frequent in June/July, and higher-intensity CHD events were more frequent in July/August. From 1961 to 2020, CHD events occurred in 69% of the years of reduced meteorological yield, and reduced meteorological yield occurred in 41% of the years with CHD events. Maize solar-induced chlorophyll fluorescence (SIF), an index of photosynthesis, was sensitive to temperature (negatively correlated) and precipitation (positively correlated). The CHD events slowed the increasing SIF from the three-leaf stage to the jointing stage, and they stopped the increasing SIF or decreased it at the tasseling–flowering to silking stages. Therefore, maize photosynthesis may be most sensitive to CHD during the flowering to silking stages, and CHD during the silking to milk stages may have the greatest impact on maize yield. Understanding the effects of CHD on maize growth/yield provides a scientific basis for reducing its negative impacts on maize production. [ABSTRACT FROM AUTHOR]

Dry spells occurring during the rainy season have significant implications for agricultural productivity and socioeconomic development, particularly in rainfed agricultural countries such as Senegal. This study employs various chaos-theory-based tools, including the lacunarity method, rescaled analysis, and the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method, to investigate the distribution, predictability, and multiscale properties of the annual series of maximum dry spell length (AMDSL) in Senegal during the rainy season. The analysis focuses on 29 stations across Senegal, spanning the period from 1951 to 2010. The findings reveal persistent behavior in the AMDSL across nearly all stations, indicating that predictive models based on extrapolating past time trends could enhance AMDSL forecasting. Furthermore, a well-defined spatial distribution of the lacunarity exponent β is observed, which exhibits a discernible relationship with rainfall patterns in Senegal. Notably, the lacunarity exponent displays a south-to-north gradient for all thresholds, suggesting its potential for distinguishing between different drought regimes and zones while aiding in the understanding of spatiotemporal rainfall variability patterns. Moreover, the analysis identifies five significant intrinsic mode functions (IMFs) characterized by different periods, including interannual, interdecadal, and multidecadal oscillations. These IMFs, along with a nonlinear trend, are identified as the driving forces behind AMDSL variations in Senegal. Among the inter-annual oscillations, a 3-year quasi-period emerges as the primary contributor and main component influencing AMDSL variability. Additionally, four distinct morphological types of nonlinear trends in AMDSL variations are identified, with increasing–decreasing and increasing trends being the most prevalent. These findings contribute to a better understanding of the variability in annual maximum dry spell lengths, particularly in the context of climate change, and provide valuable insights for improving AMDSL forecasting. Overall, this study enhances our comprehension of the complex dynamics underlying dry spell occurrences during the rainy season and presents potential avenues for predicting and managing the AMDSL in Senegal. [ABSTRACT FROM AUTHOR]

The objectives of the work were to understand the potential future climate changes in the Mediterranean region, assess the drought tolerance of the black calla lily (Arum palaestinum Boiss.), and investigate the mechanisms associated with its ability to withstand drought conditions. The Shared Socioeconomic Pathways (SSPs) of the Intergovernmental Panel on Climate Change (IPCC) were used to predict future temperature and precipitation changes. Both the SSP2-4.5 and SSP5-8.5 scenarios predicted a general increase in minimum and maximum temperatures and a decrease in precipitation. The projected increase in minimum temperature ranged from 2.95 °C under SSP2-4.5 to 5.67 °C under SSP5-8.5. The projected increase in maximum temperature ranged from 0.69 °C under SSP2-4.5 to 3.34 °C under SSP5-8.5. The projected decrease in precipitation ranged from −1.04 mm/day under SSP2-4.5 to −1.11 mm/day under SSP5-8.5. Results indicated that drought significantly impacted the physiological responses of the black calla lily. As drought increased, the black calla lily showed a reduction in leaf characteristics and non-structural carbohydrates, while proline content and reducing sugar content were increased, enhancing drought tolerance through osmoregulation. The black calla lily tolerates drought at a total ET of up to 50%. It has the potential to adapt to expected climate change through osmoregulation or by building a carbon and nitrogen sink for stress recovery. [ABSTRACT FROM AUTHOR]

Using ERA5 reanalysis data, we conducted an EOF analysis of summer precipitation in the eastern part of southwestern China (ESWC) over the past 60 years. Our study aimed to investigate the spatial distribution characteristics and interannual variability of summer precipitation in the ESWC, as well as to reveal the possible physical mechanisms influencing its interannual variability. The results indicate that, at the interannual scale, the first two modes of summer precipitation in the ESWC exhibit a uniform pattern and a north–south dipole pattern, respectively. The maximum and minimum time coefficients of the first mode correspond to the severe flood and drought events in 1998 and 2006, respectively, indicating that their time coefficients reflect the typical events well. The tri-pole sea surface temperature anomaly in the North Atlantic, along with the sea ice anomaly near the Barents Sea, induces a quasi-zonal wave train in the upper troposphere. This wave train propagates from the tropical and mid-latitude Atlantic, traverses the mid-high latitudes of Eurasia, and reaches the eastern part of China. The wave train induces cyclonic (anticyclonic) anomalies in the north (south) of the ESWC, facilitating the convergence of high-latitude dry and cold air with warm and moist southwesterly winds from the tropical ocean. This convergence promotes increased summer precipitation in the ESWC. We provided valuable insights into the interannual variability of summer precipitation in the ESWC, shedding light on the physical mechanisms responsible for these variations. [ABSTRACT FROM AUTHOR]

Germany's increasing temperatures and droughts are significantly impacting the hydrological realm. This study examines the implications of climate change on future droughts in a representative catchment within Germany's low mountain range. Findings of this research shed light on potential impacts on future seasonal water availability, aiding decision makers and stakeholders in managing regional climate change risks. Climate and drought indices, as well as the climatic water balance, are computed and analyzed until 2100, relative to a reference period. A high emission scenario (RCP8.5) and a climate protection scenario (RCP2.6) are considered to address uncertainties. Results reveal above-average warming in the study area compared to the national average. Under the RCP8.5 scenario, the far future exhibits an average of 44 annual heat days. Despite wetter winters, extended droughts persist. Water stress intensifies in summer and autumn, with a projected 68% increase in dry period duration. The findings emphasize the necessity of adaptation strategies, as even ambitious global warming mitigation efforts require regional adaptation. The study represents the first application of a Germany-wide, bias-adjusted, and regionalized dataset at catchment level. It contributes novel insights for regional water resources management and advances understanding of climate change impacts in German low mountain range regions. [ABSTRACT FROM AUTHOR]

Agricultural production and advancement is significantly affected by climate change (CC), especially in drought-prone regions where farmers depend primarily on rainfall for their livelihoods. One of the main threats to the agricultural sector for decades has been global warming, and the sector is particularly susceptible to climatic circumstances. CC has become a crucial concern impeding sustainable development due to rapid changes in urbanization, industry, and agricultural systems. Food security, which is a worldwide concern, is another essential component connected to a country's economy and people's livelihoods. In view of these adverse consequences, the main objective of this study was to examine the impact of CC and food security on sustainable agricultural development in Pakistan. The study area was selected from twenty-four districts in two provinces of Pakistan (Khyber Pakhtunkhwa and Balochistan). Collectively, the current research offers possible solutions to the dilemmas described above, which could have a significant impact on improving efficiency and technological progress. To address these issues, we introduced a slack-based approach to quantify inefficiencies in overall agricultural production. In addition, this study further scrutinized the decomposition of specific variables. The results show that Pakistan has an agricultural inefficiency of 0.62 under CC and food security constraints, of which 0.29 is primarily attributable to food security limitations. In the end, this study will help policymakers to make appropriate decisions to minimize the impact of CC on sustainable agriculture growth and improve human living standards and food security. [ABSTRACT FROM AUTHOR]

Extreme drought has negative impacts on the health of vegetation and the stability of ecosystems. In this study, the CASA model was employed to estimate the net primary productivity of vegetation over the Yunnan Plateau. The time-lag effects on vegetation were observed within a 0–6 month period of extreme droughts using the Pearson correlation coefficient. The resistance of vegetation during extreme droughts was quantified, and the recovery capability of vegetation following these events was analyzed using the ARIMA model. Moreover, the study investigated the response of vegetation to extreme droughts across diverse altitudinal gradients. The results showed that: (1) This round of extreme drought led to a decrease in the NPP of vegetation in the Yunnan Plateau. (2) Vegetation exhibits a 1–3-month lag period in response to extreme drought, with forests showing slower responses than grasslands and shrubs and higher resistance to the drought. Except for agricultural vegetation, most other vegetation types are able to recover their productivity within a year. (3) Vegetation above 3000 m is less susceptible to the impacts of extreme drought. With increasing elevation, forests exhibit an earlier lag period in response to extreme drought and an increase in resistance, but lower elevation vegetation demonstrates better recovery from extreme drought events. Shrub vegetation shows the highest resistance at elevations between 3000–4000 m, and shrubs at middle to high elevations have better recovery capacity than those at low elevations. Grassland vegetation exhibits increased resistance to extreme drought with higher elevation and shows better recovery. Agricultural vegetation demonstrates higher resistance at middle to high elevations, with no significant elevation differences in recovery capacity. Extreme drought events not only have a lag effect on the vegetation ecosystem, but also affect its stability and resilience to future drought events. To adapt to climate change, future research should emphasize the role of small-scale climate in vegetation's response to drought. [ABSTRACT FROM AUTHOR]

Frequent droughts may have negative influences on the ecosystem (i.e., terrestrial vegetation) under a warming climate condition. In this study, the linear regression method was first used to analyze trends in vegetation change (normalized difference vegetation index (NDVI)) and drought indices (Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI)). The Pearson Correlation analysis was then used to quantify drought impacts on terrestrial vegetation in the Weihe River Basin (WRB); in particular, the response time of vegetation to multiple time scales of drought (RTVD) in the WRB was also investigated. The trend analysis results indicated that 89.77% of the area of the basin showed a significant increasing trend in NDVI from 2000 to 2019. There were also significant variations in NDVI during the year, with the highest rate in June (0.01) and the lowest rate in January (0.002). From 2000 to 2019, SPI and SPEI at different time scales in the WRB showed an overall increasing trend, which indicated that the drought was alleviated. The results of correlation analysis showed that the response time of vegetation to drought in the WRB from 2000 to 2019 was significantly spatially heterogeneous. For NDVI to SPEI, the response time of 12 months was widely distributed in the north; however, the response time of 24 months was mainly distributed in the middle basin. The response time of NDVI to SPI was short and was mainly concentrated at 3 and 6 months; in detail, the response time of 3 months was mainly distributed in the east, while a response time of 6 months was widely distributed in the west. In autumn and winter, the response time of NDVI to SPEI was longer (12 and 24 months), while the response time of NDVI to SPI was shorter (3 months). From the maximum correlation coefficient, the response of grassland to drought (SPEI and SPI) at different time scales (i.e., 6, 12, and 24 months) was higher than that of cultivated land, forestland, and artificial surface. The results may help improve our understanding of the impacts of climatic changes on vegetation cover. [ABSTRACT FROM AUTHOR]

Livestock farmers in Sub-Saharan Africa rely on rain-fed agriculture, which exposes them to the risks of agricultural drought. Agricultural drought has become a significant threat to the extreme mortality of livestock, thus negatively impacting social vulnerability and household resilience to agricultural drought and extreme events. Researchers rarely empirically assess the connection between vulnerability and resilience, which are highly related concepts. By measuring and connecting vulnerability and resilience concepts closely related to disasters such as agricultural drought, this article makes a contribution to the body of disaster literature. The study aimed to empirically examine the relationship between smallholder livestock farming households' social vulnerability and their resilience to agricultural drought. A survey of 217 smallholder livestock farmers was conducted. The Social Vulnerability Index (SVI), the Agricultural Drought Resilience Index (ADRI), and Pearson's correlation coefficient were used for data analysis. A correlation was identified between resilience to agricultural drought and social vulnerability, indicating that smallholder livestock farmers are more susceptible to harm and lack the means to rebound effectively. Unsurprisingly, the majority of resource-poor smallholder livestock farmers (79%) lack safety nets during agricultural droughts. They are less resilient and more vulnerable households, leading them to social vulnerability. This study provides input/guidance to identify farming households with high social vulnerability and less resilience to threats and their capabilities of recouping and adopting after experiencing an agricultural drought. Additionally, looking at household resilience and social vulnerability to agricultural droughts could provide a way to pinpoint at-risk areas, assisting emergency planners in directing resources and intervention programs to those areas where assistance is most likely to be needed during disasters such as agricultural droughts. This implies that thorough policy intervention programs need to be tailored toward reducing damage or finding the path to recovery. [ABSTRACT FROM AUTHOR]

The percentage precipitation anomaly was used to index the effect of drought on winter wheat grown in Henan Province for the years 2011–2020. Of interest was the effect of drought on winter wheat yield and the accurate assessment of the damage done to winter wheat by drought events in order to improve the risk management of winter wheat in the context of drought hazards. The spatial and temporal variability of winter wheat drought risk in Henan Province was determined by analysis of climate data, winter wheat yield, cultivated area, and socio-economic data across three dimensions: exposure or susceptibility to drought, economic–environmental sensitivity to drought, and capacity to resist drought. A drought vulnerability assessment model, based on the entropy value method and a comprehensive fuzzy evaluation, was developed to assess the drought vulnerability of winter wheat in Henan Province compared with the percentage precipitation anomaly model. (1) There were significant spatial differences in the frequency of the five drought categories devised. (2) Areas in which there was a high frequency of mild drought events were mainly in northern and western Henan and southwestern Henan, with the frequency ranging from 17% to 29%. (3) Areas in which there was a high frequency of moderate drought events were mainly in northwestern, central, and southeastern Henan. (4) Areas in which there was a high frequency of severe and extreme drought were mainly in Anyang in northern Henan, Zhengzhou in central Henan, and Xinyang and surrounding areas in southern Henan, with the frequency ranging from 7% to 9.70%. (5) Winter wheat drought vulnerability shows an overall annually increasing trend. The susceptibility dimension had the greatest influence of the three dimensions, followed by economic–environmental sensitivity and then drought resistance, which had the least impact. The model created in this study shows the influence of drought on winter wheat production more intuitively than a conventional fuzzy synthesis, and the results can inform decision-making in winter wheat drought risk assessment and management. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive analysis of extreme events, especially drought and wet events, spanning over the past years, evaluating their trends over time. An investigation of future projections under various scenarios such as SSP-126, SS-245, and SSP-585 for the near (2023–2048), mid (2049–2074), and far future (2075–2100) using the bias-corrected Coupled Model Intercomparisons Project 6 (CMIP6) multi-model ensemble method was also performed. The Standard Precipitation Index (SPI), a simple yet incredibly sensitive tool for measuring changes in drought, is utilized in this study, providing a valuable assessment of drought conditions across multiple timescales. The historical analysis shows that there is a significant increase in drought frequency in subdivisions such as East MP, Chhattisgarh, East UP, East Rajasthan, Tamil Nadu, and Rayalaseema over the past decades. Our findings from a meticulous examination of historical rainfall trends spanning from 1951 to 2022 show a noticeable decline in rainfall across various regions such as Uttar Pradesh, Chhattisgarh, Marathwada, and north-eastern states, with a concurrent increase in rainfall over areas such as Gujarat, adjoining regions of West MP and East Rajasthan, and South Interior Karnataka. The future projection portrays an unpredictable pattern of extreme events, including droughts and wet events, with indications that wet frequency is set to increase under extreme SSP scenarios, particularly over time, while highlighting the susceptibility of the northwest and south peninsula regions to a higher incidence of drought events in the near future. Analyzing the causes of the increase in drought frequency is crucial to mitigate its worst impacts, and recent experiences of drought consequences can help in effective planning and decision-making, requiring appropriate mitigation strategies in the vulnerable subdivisions. [ABSTRACT FROM AUTHOR]

Along with tree ring width, carbon isotopes are also good proxies for climate change. Water use efficiency (WUE) can be calculated more quickly and accurately based on carbon isotopes. In this study, according to the principle of dendroclimatology, the sequence of δ13C and WUE of tree rings of Picea meyeri are built. Pearson correlation analysis and multiple regression analysis are used to explore the response of carbon stable isotopes of Picea meyeri to climate change, which revealed the relationship between δ13C of Picea meyeri and climatic factors. Based on δ13C, we calculated the WUE of Picea meyeri and analyzed its response to climate change. The main conclusions are as follows: (1) The δ13C of Picea meyeri decreases year-by-year from 1957 to 2020, in the range from −23.89‰~−21.67‰, and the average value is −22.67‰. The water use efficiency of Picea meyeri increases in the range from 17.26~61.31, with an average of 39.45. (2) The δ13C of Picea meyeri is negatively correlated with temperature, which has the highest correlation with the temperature of the growing season (c5–c9), and its coefficient is higher than that of the mean temperature of each month. (3) There is a significant positive correlation between WUE sequence and temperature. Meanwhile, due to the effect of precipitation and temperature, the Picea meyeri is subject to drought stress to some extent. Above all, temperature is the main climatic factor affecting the δ13C and WUE of Picea meyeri on Luya mountain. [ABSTRACT FROM AUTHOR]